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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Correlation Coefficient With an Outlier\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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    "### Correlation Coefficient\n",
    "\n",
    "Here is an interactive workflow demonstrating a limitation of the correlation coefficient, a common metric for analysis the relationship between two feautures, commonly used in inferential and predictive machine learning.\n",
    "\n",
    "I have recorded a walk-through of this interactive dashboard in my [Data Science Interactive Python Demonstrations](https://www.youtube.com/playlist?list=PLG19vXLQHvSDy26fM3hDLg3VCU7U5BGZl) series on my [YouTube](https://www.youtube.com/@GeostatsGuyLectures) channel.\n",
    "\n",
    "* Join me for walk-through of this dashboard [06 Data Science Interactive: Correlation Coefficient](TBD). I'm stoked to guide you and share observations and things to try out!   \n",
    "\n",
    "* I have a lecture on [Correlation Analysis](https://www.youtube.com/watch?v=wZwYEDqB4A4&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=21) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course. Note, for all my recorded lecture the interactive and well-documented workflow demononstrations are available on my GitHub repository [GeostatsGuy's Python Numerical Demos](https://github.com/GeostatsGuy/PythonNumericalDemos).\n",
    "\n",
    "* Also, I have a lecture on [Univariate Statistics](https://www.youtube.com/watch?v=wAcbA2cIqec&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=10) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ) course.\n",
    "\n",
    "* Also, I have a lecture on [Principal Components Analysis](https://www.youtube.com/watch?v=-to3JXiae9Y&list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf&index=17) as part of my [Machine Learning](https://www.youtube.com/playlist?list=PLG19vXLQHvSC2ZKFIkgVpI9fCjkN38kwf) course.\n",
    "\n",
    "#### Bivariate Analysis\n",
    "\n",
    "Quantify the relationship between two features:\n",
    "\n",
    "* e.g., relationship between porosity and permeability, nickle and copper grade, etc.\n",
    "\n",
    "What would be the impact if we ignore this relationship and simply modeled eacf of our features independently?\n",
    "\n",
    "* no relationship beyond constraints at data locations\n",
    "* independent away from data\n",
    "* nonphysical results, unrealistic uncertainty models\n",
    "\n",
    "We must quantify relationships between our features and integrate them in our models.\n",
    "\n",
    "#### Correlation Coefficient\n",
    "\n",
    "Pearson’s Product‐Moment Correlation Coefficient\n",
    "* provides a measure of the degree of linear relationship.\n",
    "* we refer to it as the 'correlation coefficient'\n",
    "\n",
    "Let's review the sample variance of feature, $\\sigma^2_x$, where $X$ feature is represented by a set of samples a locations in our modeling space, $x(\\bf{u}_\\alpha)$,  $\\alpha = 1, \\dots, n$, but we will use the shorthand,  $x_{i}$,  $i = 1, \\dots, n$.\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_{x}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})^2}{(n-1)}\n",
    "\\end{equation}\n",
    "\n",
    "We can expand the the squared term and replace on of them with $Y$, another feature in addition to $X$.\n",
    "\n",
    "\\begin{equation}\n",
    "C_{x,y}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}{(n-1)}\n",
    "\\end{equation}\n",
    "\n",
    "We now have a measure that represents the manner in which features $X$ and $Y$ co-vary or vary together.  We can standardized the covariance by the product of the standard deviations of $X$ and $Y$ to calculate the correlation coefficent. \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{x,y}  = \\frac{\\sum_{i=1}^{n} (x_i - \\overline{x})(y_i - \\overline{y})}{(n-1)\\sigma_x \\sigma_y}, \\, -1.0 \\le \\rho_{x,y} \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Interpreting the Correlation Coefficient\n",
    "\n",
    "Given a bivariate Gaussian distribution, we can make the following interpretations of correlation coefficients:\n",
    "\n",
    "* $\\rho_{x,y} = 1.0$ - perfect positive linear relationship\n",
    "* $\\rho_{x,y} = 0.0$ - no relationship\n",
    "* $\\rho_{x,y} = -1.0$ - perfect negative linear relationship\n",
    "\n",
    "if not bivariate Gaussian this does not hold.\n",
    "\n",
    "In summary we can state that the correlation coefficient is related to the covariance as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{x,y}  = \\frac{C_{x,y}}{\\sigma_x \\sigma_y}\n",
    "\\end{equation}\n",
    "\n",
    "The correlation coefficient provides a useful metrics to quantify relationships between two features at a time. We can also consider bivariate scatter plots and matrix scatter plots to visualize multivariate data.\n",
    "\n",
    "Other applications in machine learning with correlation coefficients, e.g.:\n",
    "\n",
    "- the variance explained metric for linear regression is the correlation coefficient squared\n",
    "\n",
    "\\begin{equation}\n",
    "R^2_{X,Y} = \\left( \\rho_{x,y} \\right)^2 \n",
    "\\end{equation}\n",
    "\n",
    "- principal components analysis are calculated as the Eigen values and vectors from the features' covariance matrix, the unstandardized correlation coefficient.\n",
    "\n",
    "#### Limitations of the Correlation Coefficient\n",
    "\n",
    "These are important limitations of the Pearson's product moment correlation coefficient:\n",
    "\n",
    "1. measures the strength of the linear relationship between the two features. \n",
    "2. **sensitive to outliers**\n",
    "3. assumes finite covariance, $C_{X,Y}$, and finite variances, $\\sigma^2_X$, and $\\sigma^2_Y$. \n",
    "4. only when the features are bivariate normal does the correlation coefficient provide a complete, unique description of the relationship between the two features, this includes homoscedasticity (constant conditional variance).\n",
    "\n",
    "#### Spearman Rank Correlation Coefficient\n",
    "\n",
    "The Person's correlation coefficient is quite sensitive to outliers and depature from linear behavoir (in the bivariate sense).  We have an altenrative known as the Spearman's rank correlations coefficient. \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_{R_x,R_y}  = \\frac{\\sum_{i=1}^{n} (R_{x_i} - \\overline{R_x})(R_{y_i} - \\overline{R_y})} {(n-1)\\sigma_{R_x}\\sigma_{R_y}},  \\ \\ -1.0 \\le \\rho_{R_x,R_y} \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "The rank correlation applies the rank transform to the data prior to calculating the correlation coefficent. To calculate the rank transform simply replace the data values with the rank $R_x = 1, \\dots,n$, where $n$ is the maximum value and $1$ is the minimum value.\n",
    "\n",
    "Given the samples are sorted in ascending order:\n",
    "\n",
    "\\begin{equation}\n",
    "R_{x_i} = i, \\ \\  i = 1,\\dots, n \n",
    "\\end{equation}\n",
    "\n",
    "#### Advantages of the Spearman Rank over the Pearson Product Moment Correlation Coefficient\n",
    "\n",
    "In general, the Spearman rank correlation coefficient is more robust in the presence of:\n",
    "\n",
    "* outliers \n",
    "* monotonic nonlinearity\n",
    "\n",
    "The corelation coefficients provide useful metrics to quantify relationships between two variables at a time. We can also consider bivariate scatter plots and matrix scatter plots to visualize multivariate data. In general, current practical \n",
    "subsurface modeling is bivariate, two variables at a time.\n",
    "\n",
    "#### Demonstration of a Rank Transform\n",
    "\n",
    "What does the rank correlation coefficient see? Here's a dataset with an outlier before and after rank transform."
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import rankdata\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "sample = np.random.multivariate_normal([0.0,0.0],[[1.0,0.8],[0.8,1.0]],size = 500)\n",
    "sample[-1,0] = 5.5; sample[-1,1] = 5.5 \n",
    "sample_rank = rankdata(sample,axis=0)\n",
    "plt.subplot(121)\n",
    "plt.scatter(sample[:,0],sample[:,1],c='darkorange',alpha=0.8,edgecolor='black'); plt.xlim([-3,6]); plt.ylim([-3,6]); plt.xlabel(r'$X_1$'); plt.ylabel(r'$X_2$'); plt.title('Original Features')\n",
    "plt.scatter(sample[-1,0],sample[-1,1],c='red',alpha=1.0,s=80,edgecolor='black',zorder=100);\n",
    "plt.subplot(122)\n",
    "plt.scatter(sample_rank[:,0],sample_rank[:,1],c='darkorange',alpha=0.8,edgecolor='black'); plt.xlim([0,520]); plt.ylim([0,520]); plt.xlabel(r'$R_{X_1}$'); plt.ylabel(r'$R_{X_2}$'); plt.title('Rank Transformed Features')\n",
    "plt.scatter(sample_rank[-1,0],sample_rank[-1,1],c='red',alpha=1.0,s=80,edgecolor='black',zorder=100);\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Coefficient Dashboard\n",
    "\n",
    "To demonstrate the correlation coefficient with an outlier, I built out 1 dashboard:\n",
    "\n",
    "* change the correlation coefficient and move around a single outlier and observe the impact on the Pearson and Spearman correlation coefficients.\n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "\n",
    "That's all!\n",
    "\n",
    "#### Load the Required Libraries\n",
    "\n",
    "We will also need some standard Python packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from matplotlib.patches import Ellipse                  # plot an ellipse\n",
    "import math                                             # sqrt operator\n",
    "import random                                           # random simulation locations\n",
    "from copy import copy                                   # copy a colormap\n",
    "from scipy.stats import norm\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox\n",
    "from scipy.stats import norm                            # Gaussian distribution\n",
    "import scipy.stats as st                                # statistical methods\n",
    "plt.rc('axes', axisbelow=True)                          # set axes and grids in the background for all plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare Functions\n",
    "\n",
    "I just added a convenience function for adding major and minor gridlines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_grid(sub_plot):\n",
    "    sub_plot.grid(True, which='major',linewidth = 1.0); sub_plot.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    sub_plot.tick_params(which='major',length=7); sub_plot.tick_params(which='minor', length=4)\n",
    "    sub_plot.xaxis.set_minor_locator(AutoMinorLocator()); sub_plot.yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Correlation Coefficient with an Outlier\n",
    "\n",
    "Now let's set up our dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "# dashboard: number of simulation locations and variogram parameters\n",
    "style = {'description_width': 'initial'}\n",
    "l = widgets.Text(value='                                                  Correlation Coefficient with an Outlier, Michael Pyrcz, Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "ndata = widgets.IntSlider(min = 5, max = 1000, value = 500, step = 100, description = r'$n_{samples}$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "ndata.style.handle_color = 'gray'\n",
    "\n",
    "corr = widgets.FloatSlider(min = -1.0, max = 1.0, value = 0, step = 0.1, description = r'$\\rho_{x_1,x_2}$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "ox = widgets.FloatSlider(min = 1.0, max = 2.8, value = 1, step = 0.1, description = r'$log(x_{n+1})$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "oy = widgets.FloatSlider(min = 1.0, max = 2.8, value = 1, step = 0.1, description = r'$log(y_{n+1})$',orientation='horizontal',continuous_update=False,\n",
    "                          layout=Layout(width='600px', height='40px'))\n",
    "\n",
    "corr.style.handle_color = 'gray'\n",
    "\n",
    "uipars = widgets.HBox([ndata,corr,ox,oy],)     \n",
    "\n",
    "uik = widgets.VBox([l,uipars],)\n",
    "\n",
    "def f_make(ndata,corr,ox,oy): # function to take parameters, make sample and plot\n",
    "    nonlin_wt = 1.0\n",
    "    ox = 10**ox; oy = 10**oy\n",
    "    text_trap = io.StringIO()                           # suppress all text function output to dashboard to avoid clutter \n",
    "    sys.stdout = text_trap\n",
    "    cmap = cm.inferno\n",
    "    np.random.seed(seed = 73072)                        # ensure same results for all runs\n",
    "    mean = np.array([10,10])\n",
    "    correl = np.array([[2.0,corr*2.0],[corr*2.0,2.0]],dtype=float)\n",
    "    sample = np.random.multivariate_normal(mean,correl,size = ndata)\n",
    "    sample = np.vstack([sample,[ox,oy]])\n",
    "    slope, intercept, r_value, p_value, std_err = st.linregress(sample[:,0],sample[:,1])\n",
    "    xmin = min(-3,ox-1); xmax = max(3,ox+1); ymin = min(-3,oy-1); ymax = max(3,oy+1)\n",
    "    xmin = 1; ymin = 1; xmax = 1000; ymax = 1000\n",
    "    x1 = np.array([xmin,xmax])\n",
    "    x2 = x1*slope + intercept\n",
    "    \n",
    "    nbin = int(ndata / 10)\n",
    "    plt_scatter = plt.subplot2grid((3, 3), (1, 0), rowspan=2, colspan=2)\n",
    "    plt_x1 = plt.subplot2grid((3, 3), (0, 0), colspan=2,\n",
    "                               sharex=plt_scatter)\n",
    "    plt_x2 = plt.subplot2grid((3, 3), (1, 2), rowspan=2,\n",
    "                               sharey=plt_scatter)    \n",
    "    \n",
    "    #plt.plot([0,0],[1.0,1.0],color = 'black')\n",
    "    #plt_scatter.plot(x1,x2,color = 'black',label = r'$X_2 = f(X_1)$')\n",
    "    plt_scatter.scatter(sample[:ndata-1,0],sample[:ndata-1,1],color = 'red',alpha = 0.05,edgecolors='black',label = 'Samples')\n",
    "    #plt_scatter.scatter(ox,oy,color = 'blue',alpha = 1.0,marker='s',edgecolors='black',label = 'Outlier')\n",
    "    plt_scatter.scatter(ox,oy,s=40,marker='x',color = 'black',alpha = 1.0,zorder=100,label='Outlier')\n",
    "    plt_scatter.scatter(ox,oy,s=200,marker='o',lw=1.0,edgecolor = 'black',facecolor = 'white',alpha = 1.0,zorder=98)\n",
    "    \n",
    "    import matplotlib.ticker as ticker \n",
    "    \n",
    "    plt_scatter.set_xlabel(r'$x_1$')\n",
    "    plt_scatter.set_ylabel(r'$x_2$')\n",
    "    plt_scatter.set_xlim([xmin,xmax])\n",
    "    plt_scatter.set_ylim([ymin,ymax])\n",
    "    plt_scatter.legend(loc='upper left') \n",
    "    plt_scatter.set_xscale('log'); plt_scatter.set_yscale('log')\n",
    "    \n",
    "    corr = stats.pearsonr(sample[:,0],sample[:,1])[0]\n",
    "    plt_scatter.annotate(r'$\\rho_{x_1,x_2}$ = ' + str(np.round(corr,3)),(xmin+(xmax-xmin)*0.15,ymax-(ymax-ymin)*0.9985),size=15)\n",
    "    corrs = stats.spearmanr(sample[:,0],sample[:,1])[0]    \n",
    "    plt_scatter.annotate(r'$\\rho_{R_{x_1},R_{x_2}}$ = ' + str(np.round(corrs,3)),(xmin+(xmax-xmin)*0.15,ymax-(ymax-ymin)*0.9995),size=15)\n",
    "    \n",
    "    plt_x1.hist(sample[:,0],density = True,color='red',alpha=0.8,edgecolor='black',bins=np.logspace(np.log10(xmin),np.log10(xmax),nbin))\n",
    "    plt_x1.set_ylim([0.0,0.3]); add_grid(plt_x1)\n",
    "    plt_x1.set_xlabel(r'$x_1$'); plt_x1.set_ylabel(r'Density')\n",
    "    plt_x1.set_title(r'Bivariate Gaussian Distributed Data with $\\rho =$' + str(round(corr,2)) + ' & 1 Outlier')\n",
    "    \n",
    "    plt_x2.hist(sample[:,1],orientation='horizontal',density = True,color='red',alpha=0.8,edgecolor='black',bins=np.logspace(np.log10(ymin),np.log10(ymax),nbin))\n",
    "    plt_x2.set_xlim([0.0,0.3]); plt_x2.set_ylabel(r'$x_2$'); plt_x2.set_xlabel(r'Density'); add_grid(plt_x2)\n",
    "    plt_scatter.set_ylabel(r'$x_2$'); \n",
    "    \n",
    "    \n",
    "    locmin = ticker.LogLocator(base=10.0,subs=np.arange(0.1,0.9,0.1),numticks=12)\n",
    "    plt_scatter.yaxis.set_minor_locator(locmin); plt_scatter.xaxis.set_minor_locator(locmin)\n",
    "    plt_scatter.grid(which = 'both',axis = 'both', linewidth=0.3)\n",
    "    plt_scatter.grid(which = 'major',axis = 'both', linewidth=1)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.7, wspace=0.3, hspace=0.3); plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'ndata':ndata,'corr':corr,'ox':ox,'oy':oy})\n",
    "#interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Correlation Coefficient Demonstration\n",
    "\n",
    "* select the number of data, correlation coefficient, an outlier and compare the Pearson product-momment and Spearman's rank correlation coefficients. \n",
    "\n",
    "#### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "Select the number of samples and the Pearson product-moment correlation coefficient:\n",
    "\n",
    "* **$n_{samples}$**: number of samples, **$\\rho_{x_1,x_2}$**: the Pearson product-moment correlation\n",
    "* **$log(x_{n+1}$**, **$log(y_{n+1}$**: location of a single outlier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89f33abe60254c669db4e2e5da78dfaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                                  Correlation Coefficient with an O…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab06175f087d41e28d922363d0baaeda",
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik, interactive_plot)                            # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was an interactive demonstration of the impact of an outlier on the Pearson product moment and Spearman's rank correlation coefficients. I am providing students an opportunity to play with data analytics, geostatistics and machine learning for experiential learning.\n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Professor, The University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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